Many calibration approaches disregard the role of refinement, leading to severe loss in the utility of DNNs thus trained. We successfully presented the case of declining refinement for a wide variety of approaches tested on many different datasets. The derived relationship in equation REF  showed how improving refinement can help better calibrate the model. This provides justification for calibration observed for refinement approach of [1]}. In the appendix (REF ), we show that calibration is induced by the refinement technique proposed by [2]}.
